OpenRaiser/Intern-Atlas huggingface dataset:
Automatic Detection and Rectification of Paper Receipts on Smartphones
Venue: arXiv cs.CV 2023
Year: 2023

We describe the development of a real-time smartphone app that allows the user to digitize paper receipts in a novel way by "waving" their phone over the receipts and letting the app automatically detect and rectify the receipts for subsequent text recognition. We show that traditional computer vision algorithms for edge and corner detection do not robustly detect the non-linear and discontinuous edges and corners of a typical paper receipt in real-world settings. This is particularly the case when the colors of the receipt and background are similar, or where other interfering rectangular objects are present. Inaccurate detection of a receipt's corner positions then results in distorted images when using an affine projective transformation to rectify the perspective. We propose an innovative solution to receipt corner detection by treating each of the four corners as a unique "object", and training a Single Shot Detection MobileNet object detection model. We use a small amount of real data and a large amount of automatically generated synthetic data that is designed to be similar to real-world imaging scenarios. We show that our proposed method robustly detects the four corners of a receipt, giving a receipt detection accuracy of 85.3% on real-world data, compared to only 36.9% with a traditional edge detection-based approach. Our method works even when the color of the receipt is virtually indistinguishable from the background. Moreover, our method is trained to detect only the corners of the central target receipt and implicitly learns to ignore other receipts, and other rectangular objects. Including synthetic data allows us to train an even better model. These factors are a major advantage over traditional edge detection-based approaches, allowing us to deliver a much better experience to the user.
Learning Global-Local Correspondence with Semantic Bottleneck for Logical Anomaly Detection
Venue: arXiv cs.CV 2023
Year: 2023

This paper presents a novel framework, named Global-Local Correspondence Framework (GLCF), for visual anomaly detection with logical constraints. Visual anomaly detection has become an active research area in various real-world applications, such as industrial anomaly detection and medical disease diagnosis. However, most existing methods focus on identifying local structural degeneration anomalies and often fail to detect high-level functional anomalies that involve logical constraints. To address this issue, we propose a two-branch approach that consists of a local branch for detecting structural anomalies and a global branch for detecting logical anomalies. To facilitate local-global feature correspondence, we introduce a novel semantic bottleneck enabled by the visual Transformer. Moreover, we develop feature estimation networks for each branch separately to detect anomalies. Our proposed framework is validated using various benchmarks, including industrial datasets, Mvtec AD, Mvtec Loco AD, and the Retinal-OCT medical dataset. Experimental results show that our method outperforms existing methods, particularly in detecting logical anomalies.
NFL Career Success as Predicted by NFL Scouting Combine
Venue: arXiv cs.LG 2023
Year: 2023

The National Football League (NFL) Scouting Combine serves as a tool to evaluate the skills of prospective players and assess their readiness to play in the NFL. The development of machine learning brings new opportunities in assessing the utility of the Scouting Combine. Using machine and statistical learning, it may be possible to predict future success of prospective athletes, as well as predict which Scouting Combine tests are the most important. Results from statistical learning research have been contradicting whether the Scouting combine is a useful metric for player success. In this study, we investigate if machine learning can be used to determine matriculation and future success in the NFL. Using Scouting Combine data, we evaluate six different algorithms' ability to predict whether a potential draft pick will play a single NFL snap (matriculation). If a player is drafted, we predict how many snaps they go on to play (success). We are able to predict matriculation with 83% accuracy; however, we are unable to predict later success. Our best performing algorithm returns large error and low explained variance (RMSE=1,210 snaps; ${R}^2$=0.17). These findings indicate that while the Scouting Combine can predict NFL matriculation, it may not be a reliable predictor of long-term player success.
Decision-Making Under Uncertainty: Beyond Probabilities
Venue: arXiv cs.AI 2023
Year: 2023

This position paper reflects on the state-of-the-art in decision-making under uncertainty. A classical assumption is that probabilities can sufficiently capture all uncertainty in a system. In this paper, the focus is on the uncertainty that goes beyond this classical interpretation, particularly by employing a clear distinction between aleatoric and epistemic uncertainty. The paper features an overview of Markov decision processes (MDPs) and extensions to account for partial observability and adversarial behavior. These models sufficiently capture aleatoric uncertainty but fail to account for epistemic uncertainty robustly. Consequently, we present a thorough overview of so-called uncertainty models that exhibit uncertainty in a more robust interpretation. We show several solution techniques for both discrete and continuous models, ranging from formal verification, over control-based abstractions, to reinforcement learning. As an integral part of this paper, we list and discuss several key challenges that arise when dealing with rich types of uncertainty in a model-based fashion.
Automated Grading of Automata with ACL2s
Venue: arXiv cs.LO 2023
Year: 2023

Almost all Computer Science programs require students to take a course on the Theory of Computation (ToC) which covers various models of computation such as finite automata, push-down automata and Turing machines. ToC courses tend to give assignments that require paper-and-pencil solutions. Grading such assignments takes time, so students typically receive feedback for their solutions more than a week after they complete them. We present the Automatic Automata Checker (A2C), an open source library that enables one to construct executable automata using definitions that mimic those found in standard textbooks. Such constructions are easy to reason about using semantic equivalence checks, properties and test cases. Instructors can conveniently specify solutions in the form of their own constructions. A2C can check for semantic equivalence between student and instructor solutions and can immediately generate actionable feedback, which helps students better understand the material. A2C can be downloaded and used locally by students as well as integrated into Learning Management Systems (LMS) like Gradescope to automatically grade student submissions and generate feedback. A2C is based on the ACL2s interactive theorem prover, which provides advanced methods for stating, proving and disproving properties. Since feedback is automatic, A2C can be deployed at scale and integrated into massively open online courses.
eBPF-based Working Set Size Estimation in Memory Management
Venue: arXiv cs.PF 2023
Year: 2023

Working set size estimation (WSS) is of great significance to improve the efficiency of program executing and memory arrangement in modern operating systems. Previous work proposed several methods to estimate WSS, including self-balloning, Zballoning and so on. However, these methods which are based on virtual machine usually cause a large overhead. Thus, using those methods to estimate WSS is impractical. In this paper, we propose a novel framework to efficiently estimate WSS with eBPF (extended Berkeley Packet Filter), a cutting-edge technology which monitors and filters data by being attached to the kernel. With an eBPF program pinned into the kernel, we get the times of page fault and other information of memory allocation. Moreover, we collect WSS via vanilla tool to train a predictive model to complete estimation work with LightGBM, a useful tool which performs well on generating decision trees over continuous value. The experimental results illustrate that our framework can estimate WSS precisely with 98.5\% reduction in overhead compared to traditional methods.
Self-Paced Learning for Open-Set Domain Adaptation
Venue: arXiv cs.CV 2023
Year: 2023

Domain adaptation tackles the challenge of generalizing knowledge acquired from a source domain to a target domain with different data distributions. Traditional domain adaptation methods presume that the classes in the source and target domains are identical, which is not always the case in real-world scenarios. Open-set domain adaptation (OSDA) addresses this limitation by allowing previously unseen classes in the target domain. Open-set domain adaptation aims to not only recognize target samples belonging to common classes shared by source and target domains but also perceive unknown class samples. We propose a novel framework based on self-paced learning to distinguish common and unknown class samples precisely, referred to as SPLOS (self-paced learning for open-set). To utilize unlabeled target samples for self-paced learning, we generate pseudo labels and design a cross-domain mixup method tailored for OSDA scenarios. This strategy minimizes the noise from pseudo labels and ensures our model progressively learns common class features of the target domain, beginning with simpler examples and advancing to more complex ones. Furthermore, unlike existing OSDA methods that require manual hyperparameter $threshold$ tuning to separate common and unknown classes, our approach self-tunes a suitable threshold, eliminating the need for empirical tuning during testing. Comprehensive experiments illustrate that our method consistently achieves superior performance on different benchmarks compared with various state-of-the-art methods.
Automated crack propagation measurement on asphalt concrete specimens using an optical flow-based deep neural network
Venue: arXiv cs.CV 2023
Year: 2023

This article proposes a deep neural network, namely CrackPropNet, to measure crack propagation on asphalt concrete (AC) specimens. It offers an accurate, flexible, efficient, and low-cost solution for crack propagation measurement using images collected during cracking tests. CrackPropNet significantly differs from traditional deep learning networks, as it involves learning to locate displacement field discontinuities by matching features at various locations in the reference and deformed images. An image library representing the diversified cracking behavior of AC was developed for supervised training. CrackPropNet achieved an optimal dataset scale F-1 of 0.755 and optimal image scale F-1 of 0.781 on the testing dataset at a running speed of 26 frame-per-second. Experiments demonstrated that low to medium-level Gaussian noises had a limited impact on the measurement accuracy of CrackPropNet. Moreover, the model showed promising generalization on fundamentally different images. As a crack measurement technique, the CrackPropNet can detect complex crack patterns accurately and efficiently in AC cracking tests. It can be applied to characterize the cracking phenomenon, evaluate AC cracking potential, validate test protocols, and verify theoretical models.
Sustainability Analysis Framework for On-Demand Public Transit Systems
Venue: arXiv cs.CY 2023
Year: 2023

There is an increased interest from transit agencies to replace fixed-route transit services with on-demand public transits (ODT). However, it is still unclear when and where such a service is efficient and sustainable. To this end, we provide a comprehensive framework for assessing the sustainability of ODT systems from the perspective of overall efficiency, environmental footprint, and social equity and inclusion. The proposed framework is illustrated by applying it to the Town of Innisfil, Ontario, where an ODT system has been implemented since 2017. It can be concluded that when there is adequate supply and no surge pricing, crowdsourced ODTs are the most cost-effective transit system when the demand is below 3.37 riders/km2/day. With surge pricing applied to crowdsourced ODTs, hybrid systems become the most cost-effective transit solution when demand ranges between 1.18 and 3.37 riders/km2/day. The use of private vehicles is more environmentally sustainable than providing public transit service at all demand levels below 3.37 riders/km2/day. However, the electrification of the public transit fleet along with optimized charging strategies can reduce total yearly GHG emissions by more than 98%. Furthermore, transit systems have similar equity distributions for waiting and in-vehicle travel times.
Exploring Adversarial Attacks on Neural Networks: An Explainable Approach
Venue: arXiv cs.LG 2023
Year: 2023

Deep Learning (DL) is being applied in various domains, especially in safety-critical applications such as autonomous driving. Consequently, it is of great significance to ensure the robustness of these methods and thus counteract uncertain behaviors caused by adversarial attacks. In this paper, we use gradient heatmaps to analyze the response characteristics of the VGG-16 model when the input images are mixed with adversarial noise and statistically similar Gaussian random noise. In particular, we compare the network response layer by layer to determine where errors occurred. Several interesting findings are derived. First, compared to Gaussian random noise, intentionally generated adversarial noise causes severe behavior deviation by distracting the area of concentration in the networks. Second, in many cases, adversarial examples only need to compromise a few intermediate blocks to mislead the final decision. Third, our experiments revealed that specific blocks are more vulnerable and easier to exploit by adversarial examples. Finally, we demonstrate that the layers $Block4\_conv1$ and $Block5\_cov1$ of the VGG-16 model are more susceptible to adversarial attacks. Our work could provide valuable insights into developing more reliable Deep Neural Network (DNN) models.
Bootstrapping Dynamic Distance Oracles
Venue: arXiv cs.DS 2023
Year: 2023

Designing approximate all-pairs distance oracles in the fully dynamic setting is one of the central problems in dynamic graph algorithms. Despite extensive research on this topic, the first result breaking the $O(\sqrt{n})$ barrier on the update time for any non-trivial approximation was introduced only recently by Forster, Goranci and Henzinger [SODA'21] who achieved $m^{1/ρ+o(1)}$ amortized update time with a $O(\log n)^{3ρ-2}$ factor in the approximation ratio, for any parameter $ρ\geq 1$. In this paper, we give the first constant-stretch fully dynamic distance oracle with a small polynomial update and query time. Prior work required either at least a poly-logarithmic approximation or much larger update time. Our result gives a more fine-grained trade-off between stretch and update time, for instance we can achieve constant stretch of $O(\frac{1}{ρ^2})^{4/ρ}$ in amortized update time $\tilde{O}(n^ρ)$, and query time $\tilde{O}(n^{ρ/8})$ for a constant parameter $ρ<1$. Our algorithm is randomized and assumes an oblivious adversary. A core technical idea underlying our construction is to design a black-box reduction from decremental approximate hub-labeling schemes to fully dynamic distance oracles, which may be of independent interest. We then apply this reduction repeatedly to an existing decremental algorithm to bootstrap our fully dynamic solution.
Post-pandemic Resilience of Hybrid Software Teams
Venue: arXiv cs.SE 2023
Year: 2023

Background. The COVID-19 pandemic triggered a widespread transition to hybrid work models (combinations of co-located and remote work) as software professionals' demanded more flexibility and improved work-life balance. However, hybrid work models reduce the spontaneous, informal face-to-face interactions that promote group maturation, cohesion, and resilience. Little is known about how software companies can successfully transition to a hybrid workforce or the factors that influence the resilience of hybrid software development teams. Goal. The purpose of this study is to explore the relationship between hybrid work and team resilience in the context of software development. Method. Constructivist Grounded Theory was used, based on interviews of 26 software professionals. This sample included professionals of different genders, ethnicities, sexual orientations, and levels of experience. Interviewees came from eight different companies, 22 different projects, and four different countries. Consistent with grounded theory methodology, data collection, and analysis were conducted iteratively, in waves, using theoretical sampling, constant comparison, and initial, focused, and theoretical coding. Results. Software Team Resilience is the ability of a group of software professionals to continue working together effectively under adverse conditions. Resilience depends on the group's maturity. The configuration of a hybrid team (who works where and when) can promote or hinder group maturity depending on the level of intra-group interaction it supports. Conclusion. This paper presents the first study on the resilience of hybrid software teams. Software teams need resilience to maintain their performance in the face of disruptions and crises. Software professionals strongly value hybrid work; therefore, team resilience is a key factor to be considered in the software industry.
Moral intuitions behind deepfake-related discussions in Reddit communities
Venue: arXiv cs.CY 2023
Year: 2023

Deepfakes are AI-synthesized content that are becoming popular on many social media platforms, meaning the use of deepfakes is increasing in society, regardless of its societal implications. Its implications are harmful if the moral intuitions behind deepfakes are problematic; thus, it is important to explore how the moral intuitions behind deepfakes unfold in communities at scale. However, understanding perceived moral viewpoints unfolding in digital contexts is challenging, due to the complexities in conversations. In this research, we demonstrate how Moral Foundations Theory (MFT) can be used as a lens through which to operationalize moral viewpoints in discussions about deepfakes on Reddit communities. Using the extended Moral Foundations Dictionary (eMFD), we measured the strengths of moral intuition (moral loading) behind 101,869 Reddit posts. We present the discussions that unfolded on Reddit in 2018 to 2022 wherein intuitions behind some posts were found to be morally questionable to society. Our results may help platforms detect and take action against immoral activities related to deepfakes.
The Carbon Emissions of Writing and Illustrating Are Lower for AI than for Humans
Venue: arXiv cs.CY 2023
Year: 2023

As AI systems proliferate, their greenhouse gas emissions are an increasingly important concern for human societies. We analyze the emissions of several AI systems (ChatGPT, BLOOM, DALL-E2, Midjourney) relative to those of humans completing the same tasks. We find that an AI writing a page of text emits 130 to 1500 times less CO2e than a human doing so. Similarly, an AI creating an image emits 310 to 2900 times less. Emissions analysis do not account for social impacts such as professional displacement, legality, and rebound effects. In addition, AI is not a substitute for all human tasks. Nevertheless, at present, the use of AI holds the potential to carry out several major activities at much lower emission levels than can humans.
Robust MADER: Decentralized Multiagent Trajectory Planner Robust to Communication Delay in Dynamic Environments
Venue: arXiv cs.RO 2023
Year: 2023

Communication delays can be catastrophic for multiagent systems. However, most existing state-of-the-art multiagent trajectory planners assume perfect communication and therefore lack a strategy to rectify this issue in real-world environments. To address this challenge, we propose Robust MADER (RMADER), a decentralized, asynchronous multiagent trajectory planner robust to communication delay. RMADER ensures safety by introducing (1) a Delay Check step, (2) a two-step trajectory publication scheme, and (3) a novel trajectory-storing-and-checking approach. Our primary contributions include: proving recursive feasibility for collision-free trajectory generation in asynchronous decentralized trajectory-sharing, simulation benchmark studies, and hardware experiments with different network topologies and dynamic obstacles. We show that RMADER outperforms existing approaches by achieving a 100% success rate of collision-free trajectory generation, whereas the next best asynchronous decentralized method only achieves 83% success.
NeRFlame: FLAME-based conditioning of NeRF for 3D face rendering
Venue: arXiv cs.CV 2023
Year: 2023

Traditional 3D face models are based on mesh representations with texture. One of the most important models is FLAME (Faces Learned with an Articulated Model and Expressions), which produces meshes of human faces that are fully controllable. Unfortunately, such models have problems with capturing geometric and appearance details. In contrast to mesh representation, the neural radiance field (NeRF) produces extremely sharp renders. However, implicit methods are hard to animate and do not generalize well to unseen expressions. It is not trivial to effectively control NeRF models to obtain face manipulation. The present paper proposes a novel approach, named NeRFlame, which combines the strengths of both NeRF and FLAME methods. Our method enables high-quality rendering capabilities of NeRF while also offering complete control over the visual appearance, similar to FLAME. In contrast to traditional NeRF-based structures that use neural networks for RGB color and volume density modeling, our approach utilizes the FLAME mesh as a distinct density volume. Consequently, color values exist only in the vicinity of the FLAME mesh. This FLAME framework is seamlessly incorporated into the NeRF architecture for predicting RGB colors, enabling our model to explicitly represent volume density and implicitly capture RGB colors.
Zero-Shot Object Searching Using Large-scale Object Relationship Prior
Venue: arXiv cs.RO 2023
Year: 2023

Home-assistant robots have been a long-standing research topic, and one of the biggest challenges is searching for required objects in housing environments. Previous object-goal navigation requires the robot to search for a target object category in an unexplored environment, which may not be suitable for home-assistant robots that typically have some level of semantic knowledge of the environment, such as the location of static furniture. In our approach, we leverage this knowledge and the fact that a target object may be located close to its related objects for efficient navigation. To achieve this, we train a graph neural network using the Visual Genome dataset to learn the object co-occurrence relationships and formulate the searching process as iteratively predicting the possible areas where the target object may be located. This approach is entirely zero-shot, meaning it doesn't require new accurate object correlation in the test environment. We empirically show that our method outperforms prior correlational object search algorithms. As our ultimate goal is to build fully autonomous assistant robots for everyday use, we further integrate the task planner for parsing natural language and generating task-completing plans with object navigation to execute human instructions. We demonstrate the effectiveness of our proposed pipeline in both the AI2-THOR simulator and a Stretch robot in a real-world environment.